Detecting anomalies in a scanning signal

ABSTRACT

An apparatus for scanning a track on a record carrier has a head ( 22 ) and front end circuitry ( 31 ) for scanning the track and generating scanning signals. A detection unit ( 32 ) detects anomalies in the scanning signal, for example for adjusting a tracking servo. The detection unit ( 32 ) calculates a mean value of the scanning signal and compares the mean value to a threshold for providing an anomaly detection signal ( 33 ). Further a classification unit ( 34 ) provides an indication of the type of defect for determining a suitable responsive or corrective action.

The invention relates to a device for scanning a track on a recordcarrier.

The invention further relates to a method of scanning a track on arecord carrier.

In data storage and retrieval systems servo loops are applied forcontrolling the position of a head with respect to the track on therecord carrier. Scanning signals are derived from detectors in theoptical pickup system and based thereon servo control signals aregenerated to drive actuators for positioning the head or elementsthereof. However large disturbances of the servo signals may begenerated by contamination or damage of the surface of the recordcarrier. For preventing such disturbances anomalies in the scanningsignals are to be detected.

A device for scanning a track and reading information is known frompatent application WO 00/17876. The device generates a read signal. Thedocument describes detecting disturbances in the read signal bydetermining an upper and a lower envelope signal value in apredetermined time interval. During the following time interval adifference value is computed by subtracting from each other the valuesof the lower from the upper envelope signals. A disturbance is detectedin the event that the difference value is smaller than a predeterminedborder value. A problem of the known method for detecting disturbancesis that the detection is based on the amount of HF modulation of theread signal due to marks in the track, which HF modulation variessubstantially due to a multitude of effects. For example the calculationof upper and lower envelope signals may be unreliable because noisespikes will immediately influence the calculated values.

Therefore it is an object of the invention to provide a device andmethod for more reliably detecting disturbances in signals from thehead.

To this end, according to the invention, a device for scanning a trackon a record carrier is provided, which device comprises

-   a head for scanning the track,-   a front-end unit coupled to the head for generating at least one    scanning signal, and a detection unit for detecting anomalies in the    scanning signal,-   the detection unit being arranged for calculating a mean value of    the scanning signal and comparing the mean value to a threshold for    providing an anomaly detection signal. The effect of comparing the    mean value to a threshold is that a deviation of the mean value from    its expected value is detected. The threshold may for example be    based on a long term average of the mean value plus or minus a    detection level.

The invention is also based on the following recognition. The prior artsystem provides detection of anomalies on the data signal by monitoringthe amplitude of the BF modulation signal components caused by marks inthe track. Surprisingly the inventors have found that the mean valueprovides a very good and early indicator of anomalies for scanningsignals, in particular scanning signals from optical discs like CD orDVD. The mean value of the scanning signals starts to deviate at thebeginning of a damaged or contaminated area on the disc, even before asignificant change in the BF modulation can be detected. This has theadvantage that in a very early stage the servo control loop can bemanipulated, e.g. by temporarily interrupting the input of the servoerror signals. It is to be noted that detecting the anomaly a few μsecearlier is relevant for adjusting commonly used servo loops. Earlyadjustment prevents generating large actuator drive signals which wouldnormally result from the differentiating elements in such servo controlloops.

In an embodiment of the device the detection unit is arranged forcalculating said mean value for a predetermined interval, in particularby summing a predetermined number of samples of the scanning signal.Using a predetermined interval has the advantage that the mean value canbe easily calculated at the arrival of a new signal value, and the meanvalue will respond quickly to a change in the signal. In particularsimply summing the signal values of a number of consecutive samplesprovides a computationally easy way of generating a value indicative ofthe mean.

In an embodiment of the device the front-end unit comprises means forgenerating as the scanning signal a mirror signal indicative of theamount of radiation from a radiation beam reflected via the track, inparticular by combining signals from a multitude of detector segments.This has the advantage that the mean value of the mirror signal, whichpreferably includes signals from every detector available, reliablyindicates a damaged or contaminated area on the record carrier.

An embodiment of the device comprises a servo unit for controlling theposition of the head or scanning elements of the head in dependence ofthe scanning signal, and for adjusting said controlling in dependence ofthe anomaly detection signal, in particular for interrupting thescanning signal during an anomaly. This has the advantage that, in theevent of a damaged or contaminated area on the disc, the disturbance ofthe servo function is reduced.

In an embodiment of the device the detection unit comprisesclassification means for generating a classification result of adetected anomaly by identifying the detected anomaly among a pluralityof predetermined anomaly classes by comparing the scanning signal with aplurality of reference signals corresponding to said plurality ofpredetermined anomaly classes. In an embodiment of the device theclassification means are arranged for determining at least onecharacteristic value of the scanning signal during the anomaly andcomparing the at least one characteristic value to correspondingcharacteristic values of a set of predetermined anomaly classes. Themeasures have the advantage that different classification results can beused for different responsive actions, for example the servo system maybe adjusted differently, or a specific error message may be displayedfor the user, e.g. ‘please clean disc’.

In an embodiment of the device the classification means are arranged fordetermining as characteristic values at least one of the following: amean value, a duration, a peak value, a distribution of sample values ofthe scanning signal in a predetermined number of amplitude bands.Calculating said distribution proves to be an easily computable way ofdetermining characteristic features of a signal shape.

In an embodiment of the device the classification means are arranged forgenerating the classification result at a classification timesubstantially after the anomaly detection signal indicates an anomaly.This has the advantage that detection can be optimized for early warningand classification can be optimized for reliably indicating the type ofdisturbance at a later instant.

Further preferred embodiments of the device according to the inventionare given in the further claims.

These and other aspects of the invention will be apparent from andelucidated further with reference to the embodiments described by way ofexample in the following description and with reference to theaccompanying drawings, in which

FIG. 1 a shows a record carrier (top view),

FIG. 1 b shows a record carrier (cross section),

FIG. 2 shows a scanning device having anomaly detection,

FIG. 3 shows two scanning signals,

FIG. 4 shows a detector model,

FIG. 5 shows a FIR filter for calculating a mean value,

FIG. 6 shows a defect model,

FIG. 7 shows properties of a defect signal used for signal mapping,

FIG. 8 shows a dendrogram, obtained with the agglomerative, hierarchicalalgorithm,

FIG. 9 shows the data signal contained in each cluster together with thedetermined class models,

FIG. 10 shows a defect detect and classification unit, and

FIG. 11 shows a classification process.

In the Figures, elements which correspond to elements already describedhave the same reference numerals.

FIG. 1 a shows a disc-shaped record carrier 11 having a track 9 and acentral hole 10. The track 9, being the position of the series of (tobe) recorded marks representing information, is arranged in accordancewith a spiral pattern of turns constituting substantially paralleltracks on an information layer. The record carrier may be opticallyreadable, called an optical disc, and has an information layer of arecordable type. Examples of a recordable disc are the CD-R and CD-RW,and writable versions of DVD, such as DVD+RW, and the high densitywritable optical disc using blue lasers, called Blue-ray Disc (BD).Further details about the DVD disc can be found in reference: ECMA-267:120 mm DVD—Read-Only Disc —(1997). The information is represented on theinformation layer by providing optically detectable marks along thetrack, e.g. pits or crystalline or amorphous marks in phase changematerial. The track 9 on the recordable type of record carrier isindicated by a pre-embossed track structure provided during manufactureof the blank record carrier. The track structure is constituted, forexample, by a pregroove 14 which enables a read/write head to follow thetrack during scanning. The track structure comprises positioninformation, e.g. addresses.

FIG. 1 b is a cross-section taken along the line b-b of the recordcarrier 11 of the recordable type, in which a transparent substrate 15is provided with a recording layer 16 and a protective layer 17. Theprotective layer 17 may comprise a further substrate layer, for exampleas in DVD where the recording layer is at a 0.6 mm substrate and afurther substrate of 0.6 mm is bonded to the back side thereof. Thepregroove 14 may be implemented as an indentation or an elevation of thesubstrate 15 material, or as a material property deviating from itssurroundings.

FIG. 2 shows a scanning device having anomaly detection. The device isprovided with means for scanning a track on a record carrier 11 whichmeans include a drive unit 21 for rotating the record carrier 11, a head22, a servo unit 25 for positioning the head 22 opposite the track, anda control unit 20. The head 22 comprises an optical system of a knowntype for generating a radiation beam 24 guided through optical elementsfocused to a radiation spot 23 on a track of the information layer ofthe record carrier. The radiation beam 24 is generated by a radiationsource, e.g. a laser diode. The head further comprises (not shown) afocusing actuator for moving the focus of the radiation beam 24 alongthe optical axis of said beam and a tracking actuator for finepositioning of the spot 23 in a radial direction on the center of thetrack. The tracking actuator may comprise coils for radially moving anoptical element or may alternatively be arranged for changing the angleof a reflecting element. The focusing and tracking actuators are drivenby actuator signals from the servo unit 25. For reading the radiationreflected by the information layer is detected by a detector of a usualtype, e.g. a four-quadrant diode, in the head 22 for generating detectorsignals coupled to a front-end unit 31 for generating various scanningsignals, including a read signal, a tracking error signal and a focusingerror signal. The error signals are coupled to the servo unit (25) forcontrolling said tracking and focusing actuators. The read signal isprocessed by read processing unit 30 of a usual type including ademodulator, deformatter and output unit to retrieve the information.

The control unit 20 controls the scanning and retrieving of informationand may be arranged for receiving commands from a user or from a hostcomputer. The control unit 20 is connected via control lines 26, e.g. asystem bus, to the other units in the device. The control unit 20comprises control circuitry, for example a microprocessor, a programmemory and interfaces for performing the procedures and functions asdescribed below. The control unit 20 may also be implemented as a statemachine in logic circuits. In an embodiment the control unit performsthe functions of detecting and/or classifying anomalies as describedbelow.

The device comprises a detection unit 32 for detecting anomalies in thescanning signal as follows. An anomaly detection signal 33 is generatedin the event that an anomaly is detected. For detecting a mean value ofthe scanning signal is calculated. The mean value can be a slidingaverage of the signal values. The mean value is compared to a thresholdas described below, for example a long term mean value. If thedifference of the threshold and the calculated mean value of the signalexceeds a predetermined detection level the anomaly signal is set to anactive state. In an embodiment the detection unit 32 calculates the meanvalue for a predetermined interval. The interval is selected to belonger then common periodic signal components, e.g. the longest mark inthe track. In an embodiment a value indicative of the mean is calculatedby summing a predetermined number of samples of the scanning signal. Itis noted that the DC component in such a mean value signal is to betaken into account when setting the threshold.

In an embodiment the front-end unit 31 has a combination circuit thatadds signals from several detectors for generating as the scanningsignal a mirror signal MIRN indicative of the amount of radiation from aradiation beam reflected via the track. In an embodiment the combinationcircuit combines signals from every available detector segment.

In an embodiment the servo unit 25 has a unit for adjusting the servocontrol function when the anomaly detection signal 33 is activated. Theadjustment is acting on the actuator signals, for example maintainingthe actual values until the end of the anomaly. In an embodiment theerror signals derived from the scanning signal are interrupted, i.e.made zero, during an anomaly. Alternatively the servo control unit has aunit for providing actuator signals based on extrapolating the errorsignals up to the anomaly. In an embodiment the servo unit 25 the unitfor adjusting has an input for a classification result as describedbelow. The type of adjustment is selected based on the classification,for example resuming the normal servo operation during an anomaly of aless disturbing type.

In an embodiment of the detection unit comprises a classification unit34 for generating a classification result of a detected anomaly. Theanomaly is classified to be of a certain type corresponding to one of anumber of classes. The classification result is generated at aclassification time after the anomaly detection signal indicates ananomaly. Hence a period of time is available for processing. It has beenobserved that, at least for optical disc scanning signals, directlydetecting specific types of disturbance, e.g. by a maximum likelihoodestimator, is significantly slower than first detecting the anomaly by amean value deviation, and subsequently classifying the anomaly.

First a number of characteristic values are determined of the scanningsignal during the anomaly, for example a mean value, a duration, a peakvalue, a distribution of sample values of the scanning signal in apredetermined number of amplitude bands. Then the determinedcharacteristic values are comparedto corresponding characteristic valuesof a set of the predetermined anomaly classes, e.g. by calculating adistance in a multidimensional space. In an embodiment theclassification result is generated as soon as said comparison for one ofthe anomaly classes indicates a difference that is smaller than thedifference values for the remaining anomaly classes by at least apredefined threshold.

In an embodiment the device is provided with means for recordinginformation on a record carrier of a type, which is writable orre-writable, for example CD-R or CD-RW, or DVD+RW or BD. The devicecomprises write processing means for processing the input information togenerate a write signal to drive the head 22, which means comprise aninput unit 27, and modulator means comprising a formatter 28 and amodulator 29. For writing information the radiation is controlled tocreate optically detectable marks in the recording layer. The marks maybe in any optically readable form, e.g. in the form of areas with areflection coefficient different from their surroundings, obtained whenrecording in materials such as dye, alloy or phase change material, orin the form of areas with a direction of magnetization different fromtheir surroundings, obtained when recording in magneto-optical material.

Writing and reading of information for recording on optical disks andformatting, error correcting and channel coding rules are well-known inthe art, e.g. from the CD or DVD system. In an embodiment the input unit27 comprises compression means for input signals such as analog audioand/or video, or digital uncompressed audio/video. Suitable compressionmeans are described for video in the MPEG standards, MPEG-1 is definedin ISO/IEC 11172 and MPEG-2 is defined in ISO/IEC 13818. The inputsignal may alternatively be already encoded according to such standards.

In an embodiment of the writing device the front-end unit 31 and thedetection unit 32 include a switch for setting a different signal as thescanning signal and/or a different threshold for comparing the meanvalue in the event that the device is in writing mode.

Further embodiments of the detection and classification function aredescribed below with reference to FIG. 3 to 11.

FIG. 3 shows two scanning signals. The upper trace 38 is a highfrequency signal and the lower trace 39 is a radial error signal,influenced by a disc defect in the area indicated by arrow 37. It isnoted that one particular group of disturbances can be identified for anoptical disc drive that has to do with the quality of the optical disc.This quality can severely deteriorate due to incorrect or incautioushandling of the discs by the user or the quality is bad from the startwhen the discs are poorly produced. One can think of scratches, dirtspots and fingerprints that arise on the polycarbonate substrate or theanomalies and impurities that are included in the substrate layer. Thesedisc related phenomena are called disc defects, which defects arelocally present on a disc and will distort the reflection of the laserbeam. Hence they result in abnormal photoelectric signals that in turnwill affect the generation of HF and servo signals and the behavior ofall drive elements relying on these signals. The HF signal is ftherinfluenced by the geometry of the impressed pits and the sequence inwhich they appear on the disc. Anomalies in this pit/land structure areof a different origin and hence they will be excluded from the group ofdisturbances called disc defects. The following definition of the termdisc defect will be used: disc defects are those features locallypresent on or in an optical disc that result in different behavior oftrack signals than what can be expected from the geometry of theinformation track and the dimensions or shape of the disc. Note thatphenomena such as eccentricity, tilt and skew are excluded by thisdefinition. Disc defects are subdivided in classes based on standardizeddefects, such as black dots, fingerprints and scratches.

An optical disc drive is equipped with several servo controllers thatassure the correct positioning of the laser spot on the informationtrack. For accurate tracking the controller has to respond strongly tolarge position errors, which can be achieved by using a high bandwidthcontroller. Disc defects also result in, sometimes large, positionerrors. Since these errors are unreliable, ideally the controller shouldnot respond to them at all, which implies a low bandwidth controller.Even the use of more sophisticated controllers cannot improveplayability with respect to disc defects enough without sacrificingtracking performance. This is simply due to the fact that as soon as adisc defect occurs, the photoelectric track signals become severelydistorted. Possible scanning signals are the various servo signals suchas the normalized radial error and focus error (REN and FENrespectively), the normalized mirror signal (MIRN) that is a measure forthe total amount of laser light received by the photodetector, thenormalized tilt signal and the BF data signals. From experiments itbecame clear that both the MIRN and BF signal behavior show the mostdirect relation with incoming disc defects. The HF signal contains ahigh frequency component that carries the digital data. This componentcan be regarded as noise when investigating disc defect influences thatoccur in a lower frequency range. Next to the MIRN signal it is usefulto monitor the behavior of the REN and FEN signals since these signalsare directly involved in the positioning of the laser spot. It is clearthat the REN and FEN signals are not reliable during the occurrence of adisc defect. The fact that the laser spot position is adjusted by aclosed-loop control system increases this uncertainty. Hence care mustbe taken when analyzing these signals, but they can be used fordetecting the start of a disc defect. The succes of adjusting the servocontrol depends on the ability to detect specific disturbances in timeto take the required countermeasures. When information on the type ofdefect is available it further becomes possible to select the mostsuitable strategy. Hence detection and, closely related, identificationof disc defects are discussed below.

Detection of disc defects is basically testing two hypothesis, i.e.given a signal record (y₁,y₂, . . . ,y_(k)) decide which of the twohypotheses H₀ (no defect) or H₁ (defect) is true. Online detectionimplies a causal system. This implies that it is impossible to detect ananomaly precisely at the moment that it occurs. Some delay Δt isinherently present between the detection at t=t_(s)+Δt and the actualoccurrence at t=t_(s) of the anomaly. The goal of a detection system nowis to detect a change as quickly as possible after it has occurred, inorder that, at each time instant, at most one change has to be detectedbetween the previous detection and the current time point k. It is notedthat the detection problem may further include classification oridentification. In both cases the behavior of a dynamic system,represented by a signal, is compared with known types of behavior. Basedon this comparison a decision is made according to predefined rules. Fordetection the decision is whether there is an anomaly present or notwhile in the case of classification or identification we decide to whichclass the behavior (signal) belongs as explained below. Decoupling ofthe detection and identification of disc defects implies one algorithmthat is able to detect all different types of disc defects. This relaxesthe need for fast defect identification and hence identificationsaccuracy can be improved. However the chance of false alarms during thedefect detection increases. Since the detector must be able to detectall defects its resolution will be reduced. This makes it harder todistinguish disc defects from other signal distortions. However when thecountermeasures initiated by defect detection do not endanger the properfunctioning of the drive in case of a false alarm, the decreasedreliability of the detector becomes of less importance.

FIG. 4 shows a detector model. A transition mechanism 42 maps thehypotheses for a given source 41 into an observation space 43. Thismapping follows from the criteria to which the detection must comply.The selection of the valid hypothesis is done by applying a decisionrule 44 to the mapping result. The detection method discussed here usesthe MIRN signal as input. The task of the detector will be to detectwhether the influence of a defect, represented by a reference signaly_(c)(k), is present in the input. First two corresponding hypotheses H₀and H₁ for the disc defect detection problem are defined. The nullhypothesis H₀ states that no disc defect is present and H₁ is true whena disc defect is present. The observations of the MIRN signal under thetwo hypotheses are:H ₀ : y(ts+k)=y _(n)(ts+k)H ₁ : y(ts+k)=y _(n)(ts+k)+y _(c)(k)with k=1, 2, . . . , N determining the detection windowy(t_(s))=(y(t_(s)+1), y(t_(s)+2), . . . , y(t_(s)+N)). The signaly_(c)(k) denotes the reference signal and ts is the defect arrival time.The observations of the MIRN signal and the defect signal are jointlycalled source 41. Attached to the two hypotheses are the two conditionalprobability densities Py(t_(s))|H₀(y|H₀) and Py(t_(s))|H₁(y|H₁). Theydefine the chance on respectively H₀ and H₁, given the actualobservations of ys. In order to determine which of the two hypotheses istrue a decision rule is needed. The requirement for such a rule is thatit maximizes the reliability of the decision for a given detection time.Stated differently it must minimize the detection time for a given levelof reliability. It is assumed that the chance of a false alarm and thatof a missed detection are directly related to the detection time or,with a given sample time, the size of the detection window. In thatsituation the likelihood ratio test yields an optimal decision rule withrespect to those criteria. It is defined as:$\frac{p_{{y{(t_{s})}}❘{H_{1}{({y❘H_{1}})}}}}{p_{{y{(t_{s})}}❘{H_{0}{({y❘H_{o}})}}}} >^{H_{1}} \leq^{H_{0}}\eta$where H₁ is accepted when the ratio in the left-hand side is greaterthan the threshold η. Else H₁ is rejected, indicating that no defect isdetected. The likelihood ratio forms the probabilistic transitionmechanism 42 while the threshold comparison is the decision rule 44 inFIG. 4. For simplicity it is assumed that the normal, unaffected MIRNsignal is an uncorrelated, zero-mean stochastic process (Gaussian whitenoise) with variance λ. In that case the likelihood ratio test for thepresence of a disc defect is:${\sum\limits_{k = 1}^{N}{{y\left( {t_{s} + k} \right)}{y_{c}(k)}}} >^{H_{1}} \leq^{H_{0}}{{\frac{1}{2}{\sum\limits_{k = 1}^{N}{y_{c}^{2}(k)}}} + {{\lambda \cdot \ln}\quad\eta}}$which can be written in the form of a simple discrete time FIR-filter.The detector then becomes:${\sum\limits_{k = 0}^{N}{{Y_{c}\left( {N - k} \right)}\left( {z^{- k} \cdot {Y(z)}} \right)}} >^{H_{1}} \leq^{H_{0}}{TH}$where TH denotes a new threshold value. The assumption that theunaffected EN signal can be described by Gaussian white noise is not avery realistic one. A more realistic representation can be obtained byincorporating the coloring of the noise for the quasi-stationary MIRNsignal. The choice of the threshold value TH and the detection windowsize N depend on the requirements of detection speed and reliability.These requirements on their turn depend on other elements of the opticaldisc drive such as the used control strategy during disc defects, thedata decoding and error correction algorithms.

FIG. 5 shows a FIR filter for calculating a mean value. Signal samplesare supplied to an input 51. A suitable number of sections delay thesamples and add the samples to the previous result to arrive at anoutput mean value 52 to be compared to a threshold as given in the lastformula above. The FIR-filter forms the core of the maximum likelihooddetector. Basically the output of the filter is a multiplication of Nsamples of the input signal with N corresponding samples of the assumeddefect reference signal. It is observed that the slope of the referencesignal yc(k) in the detection window is the feature that determines theamplitude of the FIR-filter response.

FIG. 6 shows a defect model. A curve 62 indicates a reference signalbased on detected defects. The defect is modeled with a reference signal61 that has an infinitely steep slope. As explained above with FIG. 5the ‘block form’ reference signal 61 provides a model for reliabledetection. The amplitude of this ‘block form’ defect model can be chosenat will, as long as the corresponding threshold value is adjustedaccordingly. Note that when the amplitude of the block form defectsignal is chosen equal to one, the output of the FIR-filter is reducedto a simple summation of N samples of the incoming MIRN signal.

Timely knowledge of the type of disc defect that is influencing theoptical disc drive makes it possible to select or adjust controlstrategies and other countermeasures to eliminate influences of discdefects on the system. Since parametric models of signals affected bydisc defects are not available, estimation methods like for instance aKalman filter, cannot be used to identify disc defects. Identificationof disc defects by comparing new signals with a database of known defectsignals resolves this problem as long as the database contains enoughmeasurements. Given the enormous number of possible disc defects thefeasibility of this method is limited by the available memory for thedatabase and the speed of algorithms to search through the stored data.The size of a database with reference signals can be reduced byidentifying a limited number of classes that each describe a large groupof defect signals in the whole data set.

After a defect is detected a separate algorithm is needed to classifythe occurring defect as stated before. This classification is performedby comparing a defect signal with a set of reference signals, eachdescribing a class of disc defects. A suitable choice for making thiscomparison is the MIRN signal. As soon as the defect filter detects adefect, a property vector p for the incoming MIRN signal is constructed.This vector contains estimates for the mean value, duration, absolutepeak value and the number of samples in several predetermined amplitudebands. At the detection time instant only N samples of the signal areavailable but for each new sample extra information becomes availableand hence the estimates of the various properties become more accurate.For the reference signals the property matrices or look-up tables,denoted by Pc, can be determined off line. Each row n, n=1, 2, 3, . . .of such a matrix holds the property vector for the first N+n−1 samplesof the corresponding reference signal. At each time instant k distancescan be calculated, for example the Euclidean distance, between theproperty vector p of the input signal and the property vectors of allthe class reference signals. When the number of available samples issufficiently high, one of these distances will become significantlysmaller, indicating a strong similarity between the correspondingreference signal and the incoming defect signal. Recognizing thissimilarity identifies the occurring disc defect on-line at an earlystage during the defect, i.e. before the end of the defect.

It is noted that for the detection and classification algorithms offsetcancellation of the MIRN signal is required. The offset can bedetermined by calculating the average value of the MIRN signal when itis unaffected by any disturbances. The required offset value can becalculated from a fixed number of unaffected samples and it can beupdated repetitively. Furthermore a good initial offset value must beavailable that, for instance, is determined during the drive'sinitialization sequence.

In an embodiment of a writing device a detector must be adapted to thelaser power adjustment. When an optical disc drive switches from writemode to read mode or vice versa, the laser is switched between high andlow power. This adjustment causes a severe change in the MIRN signallevel to which a defect detector, incorrectly, will react. An easy wayto deal with this phenomenon is to ignore the defect detector output fora short period of time whenever a laser power adjustment takes place.

FIG. 7 shows properties of a defect signal used for signal mapping. Thereference signals for the online classification algorithm representdifferent classes of disc defects. The classification process forobtaining those different disc defect classes is schematically asfollows: collecting defect data signals and preprocess (filtering andremove DC offset), signal mapping (extract a limited set ofcharacteristic properties), clustering (identify groups with similarsignal properties), class modeling (classify disc defects by describingsignals in each cluster with one descriptive signal).

The signal mapping of the defect data set is as follows. A vector withsignal properties as indicated in FIG. 7 is determined, which maps thedata signal onto a multi-dimensional property space. The firstcharacteristic property is the mean value (μy) 71 of the disc defectsignal. This value is particularly useful to distinguish between defectsthat have a higher and lower reflectivity than the normal disc. For ablack dot with a lower reflectivity than the normal disc, the mean valuewill be below zero while it will be positive for a white dot. The secondcharacteristic parameter is the duration (N) 72 that can be expressedthrough the number of measured samples when the sample time of themeasuring device is known. The third property is the peak value (max|y|) 73 of the disc defect signal. To make a fair comparison betweenvalues for all disc defects, the absolute peak value is taken. Otherwisethe peak value for a white dot would always be higher than it would befor a black dot. This is undesirable since in the focus is on thedifferent behavior of signals compared to the normal situation. Finallythe signal is divided into a fixed number of amplitude bands (Ay) 74indicated by dotted lines and count the number of samples that fallwithin each band. The resulting values for each amplitude band completethe set of characteristic parameters. It is not likely that all thesesignal properties yield a value in the same order of magnitude.Therefore weighting factors are added to all parameters in order toobtain a balanced set of signal properties.

The processes of clustering and class modeling are well known in theliterature. The clustering is based on a set of measurement signals ofdefects of various kinds, determining the characteristic values thereofresulting in vectors of properties and clustering the vectors ofproperties by determining mutual distances between these vectors. Withthe signal mapping presented above L different m-dimensional propertyvectors p_(r)=(f₁(y_(r)), f₂(y_(r)), . . . ,f_(m)(y_(r))), r=1, 2, . . ., L can be constructed. A clustering method that directly uses thegeometric interpretation of similarity is agglomerative hierarchicalclustering. The input for this clustering method is a so-calleddissimilarity entity-to-entity matrix, where each entity is consideredas a single cluster or singleton, denoted by S_(h), h in H. Note that His the set of all cluster labels and that each h is uniquely related toone cluster. For an agglomerative hierarchical clustering of L objects,the set H holds 2L-1 labels, where the first L elements correspond tothe original entities or singletons. The dissimilarity matrix can easilybe derived from the mapped data points by calculating the distancebetween every pair of objects in the data set. Various definitions forvector distances are available, such as: Euclidean distanced _(rs)={square root}{square root over ((p _(s) −p _(r))(p _(s) −p_(r))^(T))}City Block distance$d_{rs} = {\sum\limits_{j = 1}^{m}{{p_{rj} - p_{sj}}}}$and (in a more general notation) Minkowski metric$d_{rs} = \left\{ {\sum\limits_{j = 1}^{m}{{p_{rj} - p_{sj}}}^{p}} \right\}^{1/p}$The indices r and s denote the labels for the corresponding clusters.The preferred distance measure is the Euclidian distance, usuallydenoted as |p_(s)−p_(r)| With the above distance measures adissimilarity matrix D=[d_(rs)] with r,s=1,2, . . . ,L, can beconstructed. Note that D is symmetric and the elements of its maindiagonal are zero. With the dissimilarity matrix available the mainsteps of the clustering algorithm are as follows.

-   Step 1 Find the minimal value d(r*,s*), r*≠s* in the dissimilarity    matrix, and form the merged cluster S_(h)S_(r)*υS_(s)*, hεH.-   Step 2 Transform the dissimilarity matrix by substituting one new    row (and column) h for the rows and columns r*, s*, with its    dissimilarities defined as    d(r,s)F({S _(r) }, {S _(s) }, l _(r) , l _(s))    with r_(s)ε{1,2, . . . ,h}∩{r*,s*}′. F is a fixed dissimilarity    function and l_(r), l_(s) define the number of objects in cluster    S_(r) and S_(s) respectively. If the number of clusters obtained is    larger than 2, go to Step 1, else End.

The function F defines the dissimilarity between the merged clusters.Since these clusters can contain more than one object, the distancemeasures, as defined above, cannot be used here. Several methods todefine the inter-cluster distance or dissimilarity are presented below:

Nearest neighbor (Single linkage) uses the smallest distance betweenobjects in the two clusters S_(r) and S_(s).d(r,s)=min|P _(sj) −P _(ri) |, iε(1, . . . ,l _(r)), jε(1, . . . l _(s))Farthest neighbor (Complete linkage) uses the largest distance betweenobjects in the two clusters.d(r,s)=max|p _(sj) −p _(ri) |, iε(1, . . . ,l _(r)), jε(1, . . . l _(s))Average linkage uses the average distance between all pairs of objectsin the two clusters S_(r) and S_(s).${d\left( {r,s} \right)} = {\frac{1}{l_{r}l_{s}}{\sum\limits_{i = 1}^{l_{r}}{\sum\limits_{j = 1}^{l_{s}}{{p_{sj} - p_{ri}}}}}}$Centroid linkage uses the distance between the centroids of the twogroups S_(r) and S_(s).d(r,s)=∥{overscore (P)} _(s) ,−{overscore (P)} _(r)∥${\overset{\_}{p}}_{r} = {\frac{1}{l_{r}}{\sum\limits_{i = 1}^{l_{r}}p_{ri}}}$p_(s) is defined similarly.Ward linkage uses the incremental sum of squares; that is, the increasein the total within-group sum of squares as a result of merging clustersS_(r) and S_(s).${d\left( {r,s} \right)} = {l_{r}l_{s}\frac{d_{c}^{2}\left( {r,s} \right)}{l_{r} + l_{s}}}$where d_(c) ²(r, s) is the squared distance between clusters S_(r) andS_(s) defined in the Centroid linkage. The linkage methods will all givethe same or almost the same results, when applied to well-structureddata. When the structure of the data is somewhat hidden or complicated,the methods may give quit different results. In the latter case thesingle and complete linkage methods represent the two extremes of thegenerally accepted requirement that the ‘natural’ clusters must beinternally cohesive and, simultaneously, isolated from the otherclusters. Single linkage clusters are isolated but can have a verycomplex chained and noncohesive shape. In contrast the complete linkageclusters are very cohesive, but may not be isolated at all. The otherthree methods result in a trade-off between cohesiveness and isolationof the resulting clusters. The results of the agglomerative hierarchicalclustering method can be represented graphically as a hierarchicalcluster tree or dendrogram.

FIG. 8 shows a dendrogram, obtained with the agglomerative, hierarchicalalgorithm From the various options presented above the Euclideandistance measure and Ward linkage method are selected. The Euclideandistance measure is selected since it is easy to calculate and itsgeometrical interpretation is straightforward. The Ward linkage methodis chosen since it provides a good trade-off between clustercohesiveness and isolation. When compared to similar methods (averageand centroid linkage) the Ward linkage appears to result in the mostlogical clustering based on analysis of the corresponding defect signalsand their physical interpretation. In the graph the numbers at thehorizontal axis represent the indices of the original singletons andthey are called leaf nodes. The connecting horizontal lines, calledinterior nodes, represent the links between the objects. The heights ofthe vertical link lines indicate the distance between the linkedobjects. With this graphical representation of the cluster tree ansuitable number of clusters C is determined by drawing a horizontal line81 in the dendrogram. All the leaf nodes (representing the entities)that are connected below this line belong to one particular cluster. Thefinal result of the clustering process for the set of referencemeasurements can however contain inconsistencies. An example is theseparation of fingerprint signals due to small signal amplitudevariations. Choosing an appropriate weighting factor for the durationproperty can prevent this inconsistency. Another example is thecombination of black dots and white dots in the same cluster. Choosing aweighting factor for the mean value property removes this inconsistency.

The result of the clustering is a suitable number of defect classes,which are summarized in the table. Cluster Objects S₁ middle black dot700 μm middle black dot 900 μm scratch 420-820 μm S₂ middle black dot1100 μm scratch 920-1120 μm S₃ scratch 1320-1520 μm S₄ edge black dot700-900 μm quarter black dot 700-900 μm scratch at R = 32 mm S₅ scratch320 μm scratch at R = 32 mm scratch at R = 35 mm S₆ all white dots S₇all fingerprints

The final step in the procedure is generating a single description,called class model, for each cluster. The problem is to derive arepresentative signal (or class model) that adequately describes all thesignals belonging to one cluster. Inevitably a trade-off must be madebetween the accuracy of the description for individual signals and itsgeneral validity for the whole cluster. A straightforward method forthis task is to fit a function to the time series in the cluster thatapproximates the data according to some criterion. The key issues forthis approach are the choice for a general form of the function and theselection of a suitable criterion. A criterion is the sum of the squaresof the errors between the fitted function and the data points. Methodsusing this criterion are usually denoted as least squares (LS) methods.Preferably the function or model structure is based on (physical) lawsthat relate the signals to the system that generates them. When such astructure is unavailable a more general structure must be used. Examplesof such general function structures are the Fourier and Pronydecomposition that approximate the data with a sum of sinusoidal orcomplex exponential functions respectively. Other possibilities are toapproximate the data with polynomials or splines. To obtain descriptivesignals for each disc defect cluster a least squares polynomial-fittingmethod is applied. For this purpose a polynomial of a degree n=15 isfitted through the signals of a cluster, which proves to be a sufficientaccurate. However, due to the nature of the fitted function, some smalloscillations are observed in the resulting signal, which are not presentin the original time series. Especially at the edges of the defectsignal these deviations can become significant when using the defectclass signals for online detection or classification. For that purposebegin and end regions of the signal must be known as accurately aspossible. Applying a fitting routine that uses splines could resolvethis since splines offer the possibility to impose demands on the slopeof the fitted signal in regions where additional accuracy is desired.The class models for each class are mapped to a vector of characteristicvalues as described above. Finally in the classification unit a distancemeasure is determined between the vector of characteristic values andvector of characteristic values of a detected anomaly.

FIG. 9 shows the data signal contained in each cluster together with thedetermined class models. The Figure shows the multivariate 15th orderpolynomial fits for the clustered disc defect signals. The disc defectsignals for six classes (S₁υS₂, S₃, S₄, S₅, S₆, S₇) are shown in thincurves, while the fitted class models are shown as thick lines.

FIG. 10 shows a defect detection and classification unit. At an input91, samples of a scanning signal to be monitored are entered, forexample the MIRN signal as indicated above. In a filter unit 92, forexample a finite impulse response (FIR), a mean value for a monitoringperiod of the signal is calculated. The monitoring period is selected toprovide an early warning at an acceptable rate of false alarms. Animplementation of the filter unit 92 is given above in FIG. 5. Saidperiod mean value (called FIRu) is provided to a threshold detector 93which is detecting an anomaly by comparing the mean value to a detectionthreshold for producing an anomaly detection signal (called defo). Theanomaly detection signal is coupled to the various characteristic valueunits for starting the classification process. A mean value unit 94calculates the first characteristic value for the total defect. The unitreceives the mean value of the first period prior to detection (FIRu),already determined in unit 92) as starting value u₀. A duration unit 95calculates the second characteristic value of the detected defect bycounting the samples. A maximum value unit 96 calculates the thirdcharacteristic value of the defect during the defect. A signal memory 97maintains the previous samples of the scanning signal for the monitoringperiod, which are available to maximum value unit 96 for determining astarting value for said maximum. An amplitude distribution unit 98calculates the remaining characteristic values of the signal during thedefect, also receiving the previous samples from the signal memory 97.The output of the distribution unit 98 are characteristic values for 11signal value bands called bin0, bin1, . . . bin10. In a multiplexer unit99 all characteristic value are combined to a vector describing thecurrent detected defect. In an Euclidean distance unit 100 the distanceto the predetermined class model vectors is calculated. The output foreach class is provided to a final multiplexer unit 101 that feeds theresulting vector into the class determination unit 102. This unitindicates which class has the smallest distance and hence provides theclassification.

FIG. 11 shows a classification process. Vertically a determined distancevalue is given and horizontally the number of sample, i.e. the actualtime, is given. Detection occurs around sample 80, and curves markedS₁υS₂, S₃, S₄, S₅, S₆ and S₇ indicate the distance calculations for therespective classes. It can be seen that the curve S₄ indicates a smalldistance, whereas the other class curves indicate larger distances.Around sample 120 a decision can be made that the defect belongs toclass S₄.

Although the invention has been mainly explained by embodiments usingoptical disc data storages, the invention is also suitable for otherrecord carriers such as rectangular optical cards, magnetic discs or anyother type of servo controlled system that requires tracking or positioncontrol of an element. It is noted, that in this document the word‘comprising’ does not exclude the presence of other elements or stepsthan those listed and the word ‘a’ or ‘an’ preceding an element does notexclude the presence of a plurality of such elements, that any referencesigns do not limit the scope of the claims, that the invention may beimplemented by means of both hardware and software, and that several‘means’ or ‘units’ may be represented by the same item of hardware orsoftware. Further, the scope of the invention is not limited to theembodiments, and the invention lies in each and every novel feature orcombination of features described above.

1. Device for scanning a track on a record carrier, the devicecomprising a head for scanning the track, a front-end unit coupled tothe head for generating at least one scanning signal, and a detectionunit for detecting anomalies in the scanning signal, the detection unitbeing arranged for calculating a mean value of the scanning signal andcomparing the mean value to a threshold for providing an anomalydetection signal.
 2. Device as claimed in claim 1, wherein the detectionunit is arranged for calculating said mean value for a predeterminedinterval, in particular by summing a predetermined number of samples ofthe scanning signal.
 3. Device as claimed in claim 1, wherein thefront-end unit comprises means for generating as the scanning signal amirror signal indicative of the amount of radiation from a radiationbeam reflected via the track, in particular by combining signals from amultitude of detector segments.
 4. Device as claimed in claim 1, whereinthe device comprises a servo unit for controlling the position of thehead or scanning elements of the head in dependence of the scanningsignal, and for adjusting said controlling in dependence of the anomalydetection signal, in particular for interrupting the scanning signalduring an anomaly.
 5. Device as claimed in claim 1, wherein thedetection unit comprises classification means for generating aclassification result of a detected anomaly by identifying the detectedanomaly among a plurality of predetermined anomaly classes by comparingthe scanning signal with a plurality of reference signals correspondingto said plurality of predetermined anomaly classes.
 6. Device as claimedin claim 5, wherein the classification means are arranged fordetermining at least one characteristic value of the scanning signalduring the anomaly and comparing the at least one characteristic valueto corresponding characteristic values of the plurality of predeterminedanomaly classes.
 7. Device as claimed in claim 5, wherein theclassification means are arranged for calculating a distance in amultidimensional space, in particular calculating an Euclidean distance,for said comparing of characteristic values.
 8. Device as claimed inclaim 5, wherein the classification means are arranged for determiningas characteristic values at least one of the following: a mean value, aduration, a peak value, a distribution of sample values of the scanningsignal in a predetermined number of amplitude bands.
 9. Device asclaimed in claim 5, wherein the classification means are arranged forgenerating the classification result at a classification timesubstantially after the anomaly detection signal indicates an anomaly.10. Device as claimed in claim 5, wherein the classification means arearranged for generating the classification result as soon as saidcomparison for one of the anomaly classes indicates a difference that islarger than the difference values for the remaining anomaly classes byat least a predefined threshold.
 11. Device as claimed in claim 4,wherein the servo unit is arranged for adjusting said controlling alsoin dependence of the classification result, in particular for resumingthe controlling in dependence of the scanning signal during an anomalyof a less disturbing type.
 12. Method of scanning a track on a recordcarrier, the method comprising scanning the track, generating at leastone scanning signal, calculating a mean value of the scanning signal,and comparing the mean value to a threshold for providing an anomalydetection signal.